Phenotypic and Molecular Changes Over 36 Years

Resurrecting an Urban Sunflower Population: Phenotypic and Molecular Changes Over 36 Years

A THESIS SUBMITTED TO THE FACULTY OF THE UNIVERSITY OF MINNESOTA BY

Marissa Marie Spear

IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE

Briana L. Gross, Julie R. Etterson December 2019

Acknowledgements

I would like to thank my advisor, Dr. Briana Gross, for her counsel and unwavering support throughout the duration of this project. I would also like to thank my co-advisor, Dr. Julie Etterson, for sharing her scientific expertise. From experimental design to data analysis, I appreciate both my advisors’ commitment to high-quality science. I would also like to thank my committee member Dr. Amanda Grusz for her input on this project.

Additionally, I would also like to thank members of the Gross Lab, who have helped with data collection and general moral support, as well as the many undergraduate students who assisted with this project. Finally, thank you to my parents, Dwight and

Alene, for lending their engineering and crafting skills to this project.

Further thanks to the University of Minnesota Duluth Biology Department and

Integrated Biosciences Graduate Program for supporting my research.

i Abstract Resurrection experiments, in which dormant propagules of antecedent populations are grown alongside modern populations, provide a unique opportunity to directly evaluate phenotypic and molecular evolution in response to environmental challenges. To understand evolution of an urban population of Helianthus annuus (Asteraceae) over 36 years, we resurrected samples obtained from a 1980 USDA National Plant Germplasm System accession alongside contemporary successors gathered in 2016. Molecular changes in transcript expression using RNA-seq data revealed 200 differentially expressed transcripts between antecedent and modern groups. Transcript expression was higher in modern samples as compared to antecedent samples, while expression patterns indicated evolution due to genetic drift, gene introgression, or adaptive evolution. After a refresher generation in greenhouse conditions, we grew the resulting family lines in an outdoor common garden under varied water availability (high-water and low-water) and temperature conditions (ambient and elevated > 3°C) corresponding to cooler 1980 and warmer 2016 conditions to observe phenotypic differences and plastic response. Seventy- seven percent of measured traits differed, with modern individuals displaying traits similar to cultivated varieties and antecedent individuals displaying more customary wild-type traits. For example, modern plants were larger and showed more apical dominance while antecedent plants produced more branches and inflorescences. Modern trait means were often selected for across varied environmental treatments, especially those resembling modern conditions. This indicates that modern plants are well-adapted to their current environment. However, the modern population displayed little genetic variation underlying important reproductive traits which may limit the potential for further evolution of this population in response to changing conditions. The resurrection method allowed us to understand molecular and phenotypic evolution as a response to environmental pressures, gene flow from cultivated H. annuus, or some combination of evolutionary mechanisms resulting in the observed differences between the 1980 and 2016 populations.

ii Table of Contents Acknowledgements………………………………………………………………………...i Abstract……………………………………………………………………....……………ii List of Tables………………………………………………………………………….…..iv List of Figures……………………………………………………………………………..v Introduction………………………………………………………………………………..1 Methods……………………………………………………………………………………7 Results…………………………………………………………………………………....14 Discussion……………….……………………………………………………………….21 References………………………………………………………………………………..39

iii List of Tables Table 1: Environmental and genetic influence on traits……..…………..………………..28 Table 2: Selection analysis results………………….……………………………..……...30

iv List of Figures Figure 1: Sample locations ………………………………………………………….…...32 Figure 2: Common garden block design……………………………………….…………33 Figure 3: PCA biplot based on differentially expressed transcripts……….……………..34 Figure 4: Venn diagram of transcript expression………………………………….….…..35 Figure 5: Trait value distribution and direction of selection………………………..……..36 Figure 6: Genetic variation in date of first flower………………………………………..37 Figure 7: Relationships between date of first flower and seed traits………………………38

v Introduction

Species living in the Anthropocene face novel combinations of environmental

pressures and challenges due to climate change and land use change (Barnosky et al.,

2012; Matesanz, Gianoli, & Valladares, 2010). Species’ responses to changing conditions

range from adaptation or migration to extinction (Davis et al., 2005). Understanding

whether a species has the potential to adapt has major consequences for ecosystems

worldwide (Parmesan, 2006; Root et al., 2003; Hoffmann & Sgrò, 2011), and the need

for empirical evidence supporting this issue is critical. Resurrection experiments, in

which propagules of antecedent populations and modern populations are grown alongside

each other, provide a unique opportunity to directly evaluate responses to environmental

changes over time and estimate future evolutionary trajectories (Hairston et al., 1999;

Franks, Sim, & Weis, 2007; Etterson et al., 2016; Franks, Hamann, & Weis, 2018a).

The resurrection approach allows us to observe phenotypic and molecular changes

of species that have accrued over time (Etterson et al., 2016). Furthermore, if individuals are grown in contrasting environments, we can examine the extent to which adaptation proceeds through the evolution of fixed genetic changes, plasticity, or both. Treating resurrected individuals to a refresher generation allows the generation of genetic lines

with known pedigrees and family structure to tease out the influence of genetic variation

while simultaneously minimizing maternal and seed age effects (Franks et al., 2018a).

Early applications of this approach revealed genetic differentiation between successional

stages of arctic plants (McGraw, 1993) and adaptive evolution of zooplankton in

response to environmental disturbances (Hairston et al., 1999). Recent resurrection

studies have revealed contemporary evolution of phenology (Franks, Sim, & Weis, 2007;

Nevo et al., 2012; Thomann et al., 2015; Dickman et al., 2019), evolution of adaptive

plasticity (Sultan et al., 2013), and response to herbivory (Bustos‐Segura, Fornoni, &

Núñez‐Farfán, 2014; Franks, et al., 2018b). Comparisons of antecedent and modern

populations grown in the same environment allow researchers to quantify the molecular

changes underlying trait evolution (Franks et al., 2016) and genetic diversity (Nevo et al.,

2012). Direct observation of evolutionary changes can ultimately lead to a better understanding of the interplay of genetic diversity, environmental response, and plasticity as species continue to evolve.

Adaptation: Quickly changing environments can elicit rapid adaptive evolutionary

responses as populations adjust to new pressures, including those induced by climate

change (Shaw and Etterson, 2012). Traits may evolve in response to environmental

disturbance if there is genetic variation present in the population (Etterson & Shaw, 2001;

Etterson, 2004a; Etterson, 2004b), as observed in Wyeomyia smithii (pitcher plant mosquito) (Bradshaw & Holzapfel, 2001), Brassica rapa (field mustard) (Franks et al.,

2007), and Thumus vulgaris (thyme) (Thompson et al., 2013). In the short term, a substantial degree of phenotypic change can be conferred by rapid changes in gene expression rather than the evolution of the genes themselves (King & Wilson, 1975;

López-Maury, Marguerat, & Bähler, 2008; Yang & Wang, 2013). Differences in gene expression underlie adaptive trait shifts in response to stress (Swindell et al., 2007) and are important for local adaptation along climatic and latitudinal gradients (Lasky et al.,

2014; Slotte et al., 2007). Overall, whether trait means evolve via changes in gene sequence or gene expression, responses must occur rapidly to keep pace with current predictions for climate change.

Phenotypic plasticity is a universal trait (Schlichting & Smith, 2002) where in

varying environmental conditions a single genotype can result in multiple phenotypes

(Bradshaw, 1965; Schlichting, 1986). Plastic phenotypic responses to the environment are commonly maladaptive (Schlichting, 1989; Dorn, Pyle, & Schmitt, 2000; Van

Kleunen & Fischer, 2005) but can be adaptive if the reaction norm leads to the maintenance of fitness in for organisms living in a rapidly changing environment (Via &

Lande, 1985; Schlichting & Smith, 2002; Ghalambor et al., 2007). This is advantageous because the response is immediate rather than requiring dispersal and range shifts or changes in allele frequencies at structural or regulatory genes. Genotypes with adaptive plastic responses have the potential to persist in a given area even as the climate shifts

(Matesanz et al., 2010). However, there are limits on the extent of plastic responses which can lead to the eventual exhaustion of the adaptive capacity of species via phenotypic plasticity (DeWitt, Sih, & Wilson, 1998; Van Kleunen & Fischer, 2005;

Schlichting, 1986). In summary, adaptive plastic responses have the potential to buy time by allowing individual organisms to persist in their original range as natural selection acts upon populations to ultimately increase fitness over a longer time scale (Sultan, 2000;

Lande, 2009).

Phenotypic plasticity and evolution are not mutually exclusive responses to environmental change. For example, phenotypically plastic traits may be targets of selection. Boechera stricta (Drummond's rockcress) displayed adaptive plasticity of flowering time in response to temperature (Anderson et al., 2012). Earlier flowering time was also under selection, indicating that evolution may be acting upon genetic components of flowering time (Anderson et al., 2012). Plasticity itself may be a target of

selection if it has a genetic basis and environmental fluctuations are consistent (Via and

Lande, 1985). If an organism exhibits an adaptive plastic response in an altered

environment, it is more likely to pass on the genotype conferring plasticity (Nussey et al.,

2005). For example, females in a Dutch population of Parus major (great tit) exhibiting a more plastic response to temperature had the potential for a more strongly advanced egg laying date (Nussey et al., 2005). The magnitude of plasticity in laying date increased over time, suggesting that selection drove the evolution of increased plasticity associated with laying date (Nussey et al., 2005). Species with a genetic basis underlying plasticity may experience selection for a change in the degree of trait plasticity and for a shift in means of plastic traits.

Replacement and Genetic Drift: Over time, gene flow or migration into a population can lead to genetic replacement (Todesco et al., 2016). Although these mechanisms act in many situations, crop wild relatives provide a notable example where gene flow may occur from other wild populations or from their domesticated counterparts. Either of these processes will alter the genetic composition of populations and, in the case from gene flow from crops, could lead to genetic swamping (reviewed by

Ellstrand, Prentice, & Hancock, 1999). Previous studies have shown that gene flow from cultivars to wild populations can constrain local adaptation of crop wild relative populations by maintaining uniformity (Slatkin, 1987) and hybridization can replace alleles and traits within a population (Rieseberg, 2009). Alternatively, gene flow from cultivars can introduce alleles that have the potential to increase fitness, including alleles associated with increased growth rate or early flowering (Slatkin, 1987; Mercer, Andow,

Wyse, & Shaw, 2007). Hybrids may also have an adaptive advantage, as demonstrated in

wild sunflowers (Helianthus annuus × Helianthus debilis) where hybrids show faster

rates of adaptation than non-hybrids (Mitchell, 2019). Whether positive or negative, gene

flow can have a strong effect on the average phenotype of a population.

Many species have already shifted range to higher latitudes and elevations to track

climate conditions (Parmesan, 2006), but questions remain about whether the speed of

migration will be sufficient to maintain populations across fragmented landscapes

(Etterson & Shaw, 2001; Shaw & Etterson, 2012; Hodgson et al., 2012). For range shift

to be successful, there must be new habitats available, corridors connecting old and new

habitats, and wide dispersal (Shaw & Etterson, 2012; Hodgson et al., 2012).

Fragmentation can drive evolution of dispersal in adaptive and non-adaptive directions,

and populations in urban environments are commonly separated by barriers like roads and built structures (Rebele, 1994). Fragmentation drove urban populations of Crepis sancta (hawksbeard) to experience selection favoring non-dispersing seeds over dispersing seeds (Cheptou et al., 2008). This response was adaptive in urban environments where suitable habitat was limited, but may not be conducive to migration.

Plant populations in urban areas also experience reduced gene flow and greater effects of genetic drift as compared to their rural counterparts (Johnson et al., 2015). Small, urban populations face unique environmental pressures that may be exacerbated by fragmentation and constrain migration.

Contemporary Evolution of an Urban Crop Wild Relative: We used the resurrection approach to investigate how urban sunflower populations have evolved over

36 years at phenotypic and genotypic levels. The wild common sunflower (Helianthus annuus L., Asteraceae), a crop wild relative that flourishes in ruderal habitats, is a model

for studying evolutionary mechanisms (Heiser, 1951). In this study system, evolution

could have occurred via natural selection (e.g., in response to climate change), as a

consequence of genetic drift in small fragmented populations, or as a result of gene flow

from conspecific cultivars in urban flower and vegetable gardens. By growing antecedent

and modern wild sunflower populations from the same location under experimentally

manipulated environmental conditions, we ascertain the extent to which traits and

phenotypic plasticity have evolved in relation to climate change. In addition, we used

transcriptomic data from antecedent and modern populations to understand the degree of

differential gene expression evolution. By studying changes at both the phenotypic and

molecular level, we provide a holistic view of a crop wild relative evolving over 36 years

in an urban environment.

Here, we studied evolution using wild common sunflower samples collected in

1980 and 2016 from an urban environment. We applied the resurrection approach and investigated patterns of gene expression in two antecedent and two modern populations using RNA-seq data. To investigate phenotypic changes, we completed a refresher generation for one antecedent and one modern population and then grew the resulting family lines in a common garden with contrasting water and temperature treatments corresponding to 1980 and 2016 conditions. We evaluated the potential for adaptation based on trait differences these conditions relative to the direction of phenotypic selection measured in each of the different environments.

Materials and Methods

Study system

Helianthus annuus was domesticated over 4,000 years ago in eastern North

America and is now cultivated globally for its seed oil content. Native wild populations, occurring in both intact native communities and ruderal habitats, are widespread across

North America (Heiser, 1951; Crites, 1993; Seiler, Qi, & Marek, 2017). There are several key traits that differ between wild and domesticated H. annuus; domesticated H. annuus flower earlier, have apical dominance resulting a single large head, and produce larger seeds compared to the branching, many headed wild H. annuus (Heiser, 1951; Burke et al., 2002). Modern domesticated sunflowers are also self-compatible while wild sunflowers are obligate out-crossers (Burke et al., 2002). Gene flow from domesticated sunflowers to wild sunflowers is well documented (Snow et al., 1998; Linder et al., 1998;

Burke, Gardner, & Rieseberg, 2002). Wild sunflowers tend to grow in disturbed habitats like roadsides and abandoned lots (Heiser, 1951), have adapted to a wide range of environmental pressures, and, therefore, may harbor genetic variation that is useful for crop improvement (Burke, Tang, et al., 2002; Seiler et al., 2017).

Sample Collection: For antecedent-modern population comparisons, we retrieved two accessions of H. annuus seeds collected by the United States Department of

Agriculture National Plant Germplasm System (USDA-NPGS) in 1980. These seeds were originally collected from two sites in Minneapolis, MN: antecedent central (AC, PI

613744; 44°58'48'' N, 93°15'49'' W) and antecedent north (AN, PI 613745; 45°1'2'' N,

93°17'50'' W). In 2016, we returned to these coordinates and collected seeds from the nearest contemporary populations: modern central (MC; 44°58'50'' N, 93°14'34'' W) and

modern north (MN; 45°0'22'' N, 93°16'42'' W) (Figure 1). Because of intensive urban

development, modern seeds were collected from the nearest extant population.

RNA-seq methods

RNA extraction, sequencing, and alignment: Plant tissue for RNA-seq was

generated from seedlings grown directly from the 1980 USDA-NGS accessions (AC and

AN) and 2016 field collections (MC and MN). Seeds were scarified and germinated in petri dishes on damp filter paper for 4 days. Seedlings were then planted in Pro-Mix

(Quakertown, PA) and placed in a growth chamber with a 16-hour day length at the

University of Minnesota Duluth (UMD). After 4 days, the above-ground tissue

(approximately 100–200 mg) of four individuals from each subpopulation (AC, AN, MC,

MN) was harvested within one hour to avoid circadian rhythm influence on gene

expression, immediately flash frozen in liquid nitrogen, and submitted on dry ice to the

University of Minnesota Genomics Center (UMGC, http://genomics.umn.edu/).

RNA extraction, library preparation, and sequencing of the samples was

performed by the UMGC. The RNEasy Plant Mini Kit (QIAGEN) was used to extract

total RNA from 16 seedlings: 4 individuals each from the ancestral subpopulations (AC

and AN) and both modern subpopulations (MC and MN). The quantity and quality of

RNA was assessed using the RiboGreen Assay, Nanodrop Spectrophotometer, and

Agilent 2100 Bioanalyzer (Agilent). Samples yielded RNA concentrations ranging from

6.66 ng/μl to 120.64 ng/μl with RNA integrity numbers (RIN) within an acceptable range

(Supplementary Table 1). Strand-specific TruSeq RNA libraries were created for each sample using Illumina NeoPrep and paired ends sequenced by Illumina HiSeq 2500 high-

output mode at 2x125 bp. Libraries were sequenced on one-half lane. This resulted in

250,838,097 paired-end reads.

Quality assessment and read alignment was carried out by the UMGC using their

custom rnaseq-pipeline (https://bitbucket.org/jgarbe/gopher-pipelines/wiki/rnaseq-

pipeline.rst). In the pipeline, the raw read quality was assessed using FastQC (v. 0.11.5)

then filtered by Trimmomatic (v. 0.33) to remove adapters and low-quality bases using a

3 bp sliding window and minimum Q16 resulting in a final average quality of 36.07.

Filtered reads were aligned to the Helianthus annuus XRQ masked reference genome (c.

1.2) (https://www.heliagene.org/HanXRQ-SUNRISE/) using HISAT2 (v. 2.1.0). Read

counts were generated using featurecounts in the SubRead program (v. 1.34.2) in R (v.

3.3.3).

Outlier analysis: All samples were checked for extreme transcript expression

patterns to ensure conservative differentially expressed (DE) transcript identification. A

PCA biplot based on DESeq2 (v. 1.22.2) normalized counts of the top 500 DE transcripts

was used to visualize expression patterns among populations and subpopulations and

identify potential outliers. Visual outlier identification was validated using a bagplot

showing samples inside and outside the 50th percentile (Kruppa and Jung, 2017).

Differential expression analysis: Normalization and identification of DE transcripts between antecedent and modern populations (AN and AC versus MN and

MC) was carried out with the DESeq2 package (v. 1.22.2; Love et al., 2014) in R (v.

3.5.3). We identified transcripts as DE if they exhibited a fold change of >2 with a corrected p-value < 0.05. We created two lists of DE transcripts: one based on results with all samples included, while the other used a more conservative leave-one-out

approach. In our leave-one-out approach, differential expression analysis was carried out

by removing a single sample at a time to mitigate the influence of single samples on DE

transcripts. Transcripts that were significantly differentially expressed across all tests in

the leave-one-out approach were identified as DE.

GO enrichment analysis: Gene ontology (GO) enrichment of DE transcripts was

carried out using PANTHER (v. 14.1; Mi et al., 2019) on our two DE transcript sets: 1)

DE transcripts with all samples included, and 2) the conservative set including DE transcripts that were present in all leave-one-out tests. A statistical over-enrichment test using Fischer’s exact test and false discovery rate correction was used to identify over- or under-expressed gene categories. Annotations from the H. annuus XRQ reference genome (https://www.heliagene.org/HanXRQ-SUNRISE/) were provided as a custom

reference as recommended by GO Consortium (Ashburner et al., 2000).

Resurrection methods

In order to reduce environmental carryover effects from seed storage and field

conditions, one ancestral (AN) and one contemporary population (MN) were raised for a

single generation in a greenhouse at UMD (46.82° N, 92.08° W) and then crossed within

populations to produce seed for subsequent experiments. The refreshed generation was

planted into an experimental outdoor array where both water availability and temperature

were manipulated.

Minnesota has experienced an increase in summer temperatures and a decrease in

summer rainfall since 1980, and this trend is projected to continue through 2069

(Galatowitsch et al., 2009). Since 1980, temperatures in MN have increased at a rate of

0.27o C per decade (Minnesota Department of Natural Resources, n.d.-a) and heavy rain

events are now 2.5 times more compared to pre-2000 records (Minnesota Department of

Natural Resources, n.d.-b). The climate in Duluth, MN, is influenced by Lake Superior,

and is cooler and wetter than Minneapolis, MN. Duluth temperatures in 2018 were

similar to 1980 Minneapolis temperatures, while the elevated temperature treatment is

similar to modern, 2016 Minneapolis temperatures.

Refresher Generation: Seeds were germinated in Petri dishes on damp filter paper

and then grown in 14 cm x 16.5 cm pots (Greenhouse Megastore, Danville, IL, Item No.

CN-SQK) with a 1:2 sand:soil (Pro-Mix, Quakertown, PA) substrate mix. A randomized

mating design, where one plant was crossed with two other plants from the same

population, was implemented to produce families of siblings and half-siblings. Refreshed

seeds from 12 ancestral families and nine modern families were germinated on petri

dishes as previously described and 1,500 seedlings were planted in 4.8 cm x 5.9 cm plug

flats (Greenhouse Megastore, Danville, IL, Item No. CN-PLG) in a 1:2 sand:soil (Promix

BX with mycorrhizae) substrate mix. Seedlings were grown in greenhouse conditions for

three weeks, then hardened off for two weeks and transplanted into 15.2 cm x 40.6 cm

tree pots (Greenhouse Megastore, Danville, IL, Item No. CN-SS-TP) with the same

substrate mix.

Environmental treatments: Seedlings were transported to the UMD Research &

Field Studies Center (46° 49’ 2” N, 92° 5’ 13” W) and arranged in a randomized

complete block design under six 3.7 m x 7.9 m rainout shelters with polycarbonate roofs

and open sides. Each shelter contained one complete block (Figure 2). Within each

shelter, four treatments were implemented: ambient temperature + high-water, ambient temperature + low-water, elevated temperature + high-water, and elevated temperature +

low-water. Elevated temperature was imposed with hexagonal open-top chambers

(Marion et al., 1997) constructed of 1 mm thick SUN-LITE fiberglass glazing (Solar

Components Corporation) and standing 78 cm tall. These chambers produced an increase of 2.8°C (p = 0.001) relative to ambient temperature conditions. Plants in the high-water

treatment were watered weekly for the duration of the experiment, while plants under

low-water conditions were watered half as much weekly starting after four weeks in the

field. This was reduced to half as much water every other week at six weeks to simulate

late-season drought conditions.

Phenotypic Measurements: Traits were measured multiple times throughout the growing season with final measurements recorded at the end of October. Height, stem diameter, and leaf number were measured every two weeks from early June to mid-

August. Date of first bud and date of first flower were recorded. In early August, the newest fully expanded leaf of each plant was collected, flattened under glass and photographed. Leaf area was determined using Easy Leaf Area (Easlon and Bloom, 2014)

and samples were weighed after being dried for 72 hours at 80° C. Specific leaf area

(SLA) was calculated by taking the leaf area (m2) divided by dry mass (kg). Final

measurements when harvested at the end of October included height, stem diameter, and

total number of inflorescences. Inflorescences were harvested and a subset of seeds were

counted and weighed for two individuals per family per environmental treatment. For

these individuals, seeds from up to three inflorescences were counted and the average

weight of seeds from each inflorescence recorded.

Phenotypic Analysis: Differences between modern and ancestral populations as

well as temperature and watering treatments were determined using a linear mixed effect

model with REML. All analyses were performed in JMP Pro (v. 14.0, SAS Institute Inc.,

Cary, NC, 2018). Fixed effects included population age (1980 or 2016), temperature

treatment (elevated or ambient), and water treatment (high- or low-water) and all two-

way and three-way interactions. Block and family [population] were considered random effects. Phenotypic traits measured at final harvest, flowering and bud date, and seed count and weight were analyzed. To investigate the genetic variation present within antecedent and modern populations, the antecedent and modern populations were

analyzed separately using the same linear mixed effect model with REML with family as

a main effect and block as a random effect.

Selection Analysis: Preliminary analyses showed that populations responded to the environments in different ways (population x treatment interactions). Consequently, the data were divided into subsets and phenotypic selection analyses were performed separately for antecedent and modern lineages in each environmental treatment (Lande &

Arnold, 1983; Brodie, Moore, & Janzen, 1995). Relative fitness was regressed onto traits

of interest to find coefficients associated with the degree of direct linear and nonlinear

selection on individual traits and trait combinations (Lande & Arnold, 1983; Brodie,

Moore, & Janzen, 1995). Traits included in the analysis were final measurements of

height, stem diameter, SLA, and date of first flower. Other traits were not included

because they provided an overlapping measure of the same trait. For example, date of

first bud correlated closely with date of first flower. Trait values were standardized so

each had a mean of 0 and a standard deviation of 1. Relative fitness was defined for each

population within each treatment separately as the estimated total seed number (average

number of seeds per inflorescence multiplied by total inflorescence number) divided by

the maximum estimated total seed number, so that the highest fitness score was 1 and the

lowest was 0.

Direct linear selection on each trait was estimated using a linear multiple

regression model to obtain regression coefficients (selection gradient: βi) for each trait

(Lande & Arnold, 1983; Brodie, Moore, & Janzen, 1995). Nonlinear selection gradients were estimated using a separate multiple regression model including linear trait variables, quadratic variables, and cross-products trait values (Lande & Arnold, 1983; Brodie,

Moore, & Janzen, 1995). Coefficients associated with the quadratic variables (γii) and

cross-products (γij) were obtained to estimate the shape of the selection surface and the

joint effect of selection on trait combinations, respectively. We estimated the selection

differential (S) for each trait in each treatment by obtaining the covariance between

relative fitness and the traits listed above (Brodie et al., 1995). S encompasses the total

selection on a trait, including the effects of direct selection on a trait in addition to

indirect selection due to trait correlations (Russell, Lande & Arnold, 1983).

Results

RNA-seq analysis

RNA-seq alignment and differential expression analysis: After cleaning and

trimming 244,063,584 total reads, 84.79% were successfully mapped to the sunflower

reference genome (Badouin et al., 2017). Gene expression was detected in 48,809 of

56,261 genes and long non-coding RNAs (lncRNAs) in the H. annuus genome across all

samples. Gene expression patterns were similar for individuals within each

subpopulation (AN, AC, MN, MC), except for one outlier from AN (Fig. 3). Modern and

antecedent populations differed based on PC 1 and northern and central subpopulations differed based on PC 2 (Fig. 3). Analysis based on normalized transcript counts of the top

500 DE genes indicated sample AN-1 exhibited extreme gene expression patterns. This sample was 2.99 SD away from all other samples on a PCA biplot and appeared outside the 50th percentile on a bagplot (Supplementary Fig. 1). Gene expression patterns exhibited by sample AN-1 were not representative of the antecedent population and could bias the DE genes found in further analyses in favor of aberrant AN-1 expression patterns. For example, genes may be misidentified as DE due to extreme upregulation in

AN-1 compared to all other antecedent samples. Alternatively, genes may go unrecognized as DE if AN-1 expression was more similar to the modern samples than the ancestral samples. Thus, this sample was removed from all subsequent analyses.

Differential expression analysis using DESeq2 identified transcripts that were up- or down-regulated between antecedent and modern populations. Transcripts were considered differentially expressed if they differed by a fold change of 2 with an FDR corrected p-value < 0.05 in 1) a DE test with all samples except the outlier, and 2) a more conservative measure where genes appeared in all DE tests with one sample removed.

After filtering out transcripts with no or low expression (normalized transcript mean count across samples < 2) and transcripts only expressed in one sample, 35,405 genes remained. The test with all samples revealed 200 DE transcripts, with 61 up-regulated in the antecedent population and down-regulated in the modern population and 139 down- regulated in the antecedent population and up-regulated in the modern population. Sixty- eight transcripts were consistently present across all tests with one sample removed, with

26 up-regulated in the antecedent population and down-regulated in the modern

population and 42 down-regulated in the antecedent population and up-regulated in the

modern population (Supplementary Table 2). Of the 200 DE transcripts identified in the

test with all samples, 14 were expressed only in the antecedent population and 56 were

expressed only in the modern population (Fig. 4). Eighty-three transcripts were functional

genes annotated based on the BLAST2GO database, 35 were ncRNAs, and 82 were

unknown proteins (Supplementary Table 2).

Gene Ontology: Gene ontology over-representation analysis was carried out using

PANTHER (v. 14.1) to determine whether gene expression in any category varied from the antecedent population to the modern population. Of the 56,231 genes used in the DE analysis, GO IDs mapped to 51,288, and these genes were used as the reference list. DE genes with a fold change >2 (FDR < 0.05) were assigned to GO terms in order to understand function. GO terms matched to 163 of 200 DE genes. Using the full set of 200

DE genes, 13 categories were under-represented, including 2 molecular function categories, 1 biological processes category, and 10 cellular component categories

(Supplementary Table 3). Unclassified genes were significantly over-represented in molecular function, biological processes, and cellular component categories using the full set of DE genes, while no annotated groups were significantly over- or under- represented using the more conservative DE gene list, up- or down-regulated genes, or lineage specific DE genes.

Environmental Manipulation Treatments

Response of antecedent and modern lineages to contrasting environments:

Overall, antecedent and modern individuals expressed different phenotypes and responded differently to environmental treatments. During the experiment, survival from

transplanting in common outdoor conditions to harvest was high for both populations

(modern: 98.2% survival; antecedent: 99.5% survival, χ2 = 3.49, p = 0.06). Of those

survivors, 99.2% of modern plants flowered and 99.7% of antecedent plants flowered (χ2

= 1.41, p = 0.23).

Overall, modern and antecedent differed on average for seven of nine traits.

Relative to the ancestral population, the modern plants were 14 cm taller (+11.9%, p =

0.05; Fig. 5C), produced stems that were 1 mm broader (+11.9%, p = 0.0222; Fig. 5B), and had leaves with lower SLA (p < 0.0001; Fig. 5D). With respect to reproductive traits, modern plants produced buds 14 days later (p < 0.0001) and flowered 14.5 days later (p <

0.0001; Fig. 5G) when in the same environment as antecedent plants. Compared to antecedent plants, modern plants also produced 28% fewer inflorescences (p < 0.0001;

Supplementary Fig. 2) as a result of decreased branching architecture (p < 0.0001;

Supplementary Fig. 2). However, average weight per seed and average seed number per

inflorescence did not significantly differ between the two populations.

Modern and antecedent populations were initially analyzed together

(Supplementary Table 4) but then split apart due to population and environmental

treatment interactions. SLA was the only trait consistently affected by temperature across

both populations– it was lower in elevated temperatures for antecedent (p < 0.0001, Table

1A) and modern (p < 0.0001, Table 1B) populations. Differences in height and date of

first flower were only significant in antecedent populations. Antecedent plants in elevated

temperatures were 11 cm taller than those in ambient temperatures (p = 0.0004, Table

1A), while plants flowered one day earlier (p = 0.048, Table 1A) and produced buds 1.4

days earlier (p = 0.059, Table 1A) in high-water conditions compared to low-water

conditions. Additionally, antecedent plants in high-water conditions produced 1.14x more branches than those in low-water conditions (p = 0.047, Table 1A). Antecedent plants were more responsive to watering treatments compared to temperature treatments and overall more responsive to environmental treatments compared to modern plants.

Genetic line significantly affected eight of nine traits in the antecedent population, and had a significant influence on only five traits in the modern population (Table 1, family column). Branch number was the only trait unaffected by family for the antecedent population. Date of first flower, height, stem diameter, inflorescence number, and SLA all varied based on family genotype for both populations (Table 1). In addition to these traits, bud date, average seed number per inflorescence, and average weight per seed were also significantly influenced by family in the antecedent population (Table 1).

Date of first flower and initial bud date were highly plastic in the antecedent population, indicative of genetic variation between families. Antecedent families displayed different degrees of flowering time plasticity in response to combinations of water and temperature treatments (p < 0.0001, Fig. 6, Table 1A temperature x water x family), whereas the modern families did not display plasticity of flowering time among treatments (Fig. 6, Table 1B temperature x water x family). Antecedent families also

displayed bud time plasticity in response to environmental treatments (p = 0.002, Table

1A), while modern families did not.

Other measures of reproductive success displayed varied patterns of correlation

based on population. Plants that flowered earlier consistently produced more

inflorescences over the growing season for antecedent (-0.07x, t = -6.42, p < 0.0001) and

modern (-0.062x, t = -4.54, p < 0.0001) populations. For the antecedent population,

earlier flowering was also associated with fewer seeds per inflorescence (t = 8.87, p <

0.0001) (Fig.7). Additionally, when antecedent plants produced more inflorescences, the

average number of seeds per inflorescence decreased for antecedent individuals (-6.11x, t

= -2.79, p = 0.006). It is likely that the decrease in seeds per inflorescence associated with an earlier flowering date in the antecedent plants is driven by the greater number of inflorescences produced by these plants over the course of the season (i.e., more inflorescences overall, but fewer resources per inflorescence), although this was not tested in our experimental set-up. Modern plants produced the same average number of seeds per inflorescence regardless of total inflorescence number and there was no relationship between flowering time and seed number (Fig. 7). A higher seed weight was associated with early flowering plants for both antecedent (t = -6.15, p < 0.0001) and modern populations (t = -2.56, p = 0.011; Fig. 7).

Selection analyses: Overall, selection was more common in the antecedent population compared to the modern population and selection strength differed based on environmental treatments (Table 2). Larger plants were generally selected for in both populations in every environment, while estimates of selection on SLA were only significant in more stressful environments. Later date of first flower was commonly selected for in the antecedent population, but date of first flower was not usually under selection in the modern population.

Larger stem diameter was strongly selected for in both populations in every environment, while increased height was significantly selected in every population and environmental combination except for modern plants in elevated temperatures and low- water conditions (Table 2, Fig. 5). Increased stem diameter was the most strongly

selected trait in antecedent plants, and usually the most strongly selected in modern

plants. Direct selection for increased stem diameter was significant for antecedent plants

in all environments except for the elevated temperature low-water treatment, while

increased stem diameter was always directly selected for modern plants (Table 2, Fig.

5F). In elevated temperature high-water conditions, stem diameter experienced disruptive

selection in both antecedent (γii = 0.067 ± 0.035 SE, p = 0.057) and modern plants (γii =

0.108 ± 0.047 SE, p = 0.036). Height and date of first flower of modern plants in elevated

temperature high-water conditions also experienced non-linear selection (γij = 0.293 ±

0.125 SE, p = 0.032). Total selection for increased height was usually stronger in

antecedent populations compared to modern populations, except for in ambient

temperature low-water conditions. Height was only under direct selection for antecedent

individuals in elevated temperature low-water conditions and for modern individuals in

ambient temperature low-water conditions (Fig. 5G).

SLA was only under selection in environments that were non-optimal for the

populations. Increased SLA was under direct and total selection in antecedent plants in

elevated temperature high-water conditions, and was directly selected in modern plants in

ambient temperature low water conditions (Table 2, Fig. 5H). In all other environments, selection patterns in antecedent plants non-significantly favored lower SLA either directly or indirectly, while selection patterns were very weak in modern plants.

Date of first flower experienced contrasting patterns of direct and total selection under different environments. In antecedent plants, the general trend was that earlier date of first flower was marginally favored under direct selection; in contrast, total selection for later date of first flower was significant in all environments (Table 2, Fig. 5E). In

modern plants, date of first flower was never under total selection; however, direct

selection patterns trended towards earlier flowering overall, earlier flowering in ambient

high-water conditions (Table 2, Fig. 5E), and also experienced stabilizing selection (γii = -

0.247 ± 0.113 SE, p = 0.035).

Discussion

We identified molecular and phenotypic evolution in urban wild sunflower

populations that were separated by 36 years using a resurrection approach. Transcript

expression patterns varied based on location and time, and differentially expressed genes

between antecedent and modern populations were associated with disease resistance,

defense, and growth. In the common garden experiment, the modern population was

taller and had thicker stems, suggestive of cultivated varieties, while the antecedent

population displayed a more classic wild phenotype with many branches and

inflorescences. In many, but not all cases, the direction of selection on the antecedent population was in the direction of the modern phenotype. However, the degree of plastic

response was greater in the antecedent population compared to the modern population,

especially regarding date of first flower. Changes in gene expression patterns, phenotype,

and plasticity support evolution of the population via genetic drift, selection, gene flow

from cultivars, or some combination of these mechanisms.

Evidence of Differential Transcript Evolution: The distinct patterns of transcript

expression between the modern and antecedent populations is consistent with the

evolution of gene expression patterns over time. Expression patterns differentiated across

PC1 based on population location (Central vs. North) and across PC2 based on

population age (Antecedent vs. Modern; Figure 3). The grouping of populations from the same locations along PC1 despite their separation across >30 years supports both the validity of these transcriptome comparisons and also the validity of comparing the AN and MN populations in the common garden experiment. The differential transcript expression patterns between the geographically proximal locations (i.e., the distance between the Central vs. North locations on PC1) could be due to either local adaptation or genetic drift (Whitehead and Crawford, 2006), as has been suggested for patterns of differential gene expression between wild and weedy sunflowers (Lai et al., 2008). While it is impossible to eliminate the potential effects of selection due to all abiotic and biotic environmental pressures that could differ between the Central and North locations, these populations are found in generally similar environments with presumably similar selective pressures, making genetic drift the most likely mechanism driving differentiation between subpopulations separated by distance. Differential transcript expression between antecedent and modern populations could also be driven by genetic drift. However, if genetic drift were primary responsible for the changes, we would expect that populations separated by age would likely evolve gene expression patterns in unrelated directions rather than grouping together based on PC2. Since both modern populations evolved in the same direction compared to the antecedent populations, this pattern is more likely due to selection (Derome et al., 2006) or introgression from similar genetic pools.

The number of differentially expressed transcripts identified was surprisingly low compared to other studies. Our study found 200 differentially expressed transcripts between modern and antecedent samples using the full dataset, while our more stringent

measures identified just 68. Other sunflower transcriptomic studies have found over

1,000 differentially expressed genes between stressed and non-stressed plants (Liang et al., 2017). One likely explanation for the low number of DE transcripts could be that we collected data from seedlings in a controlled, non-stressful environment, and it is possible that the addition of a stressor would have revealed more differentially expressed transcripts. For genes that did show significant DE, the modern population exhibited more up-regulated genes compared to the antecedent populations (Supplementary Table

2; Figure 4). The greater number of up-regulated transcripts in modern plants may be due to faster development leading to the expression of different genes during early seedling stage; this is based on our observation that the seedlings were larger at harvest date for the RNAseq experiment and also larger at the same stage in the common garden experiment. Finally, maternal effect and seed age could have affected transcription levels as RNA material was extracted from plants before they had undergone a refresher generation.

Of the 200 differentially expressed transcripts, 13 genes were associated with plant defense and disease resistance. Resistance genes were evenly differentially expressed between the populations, with 7 up-regulated in modern populations and 6 up- regulated in antecedent populations. Plant defense and disease resistance genes experience co-evolution with pathogens and experience diversifying selection pressure

(Meyers et al., 2003). Differential expression of these genes is expected to be an adaptive response to selective pressures across short time scales (Reymond et al., 2000; Kant et al., 2008). Surprisingly, two genes associated with tobacco mosaic virus multiplication were up-regulated in modern populations. Tobacco mosaic virus does not infect

sunflowers in North America, so this identification may be due to low-quality

annotations, although there has been one reported case of tobacco mosaic virus infecting

H. annuus in Egypt (Zein, et al. 2012).

Many of the DE transcripts were lncRNAs (18%) or unannotated putative proteins

(40%). LncRNAs play a role in gene expression regulation (Chekanova, 2015),;

differential expression of non-coding RNAs could contribute to the observed differential

gene expression patterns. The sunflower genome is highly repetitive and was annotated

based on orthologous genes of other asterids and Arabidopsis (Badouin et al., 2017).

However, the genome contains many unknown, unannotated transcripts. As annotation

quality improves, the purported function of these DE genes will become more reliable.

Additionally, while RNAseq data provide insight into transcript counts, they cannot be used to detect post-transcriptional and post-translational modifications; these modification steps are often extensive and play important roles in protein production and function (Glisovic et al., 2008; Khoury, Baliban, & Floudas, 2011). Thus, although the

RNA-seq analyses revealed transcript expression differentiation and evidence of evolution among the populations, technological caveats demonstrate the need for integrated analyses. Thus, we designed a parallel investigation to elucidate phenotypic responses to environmental variables and to include fitness measures, thereby providing a more comprehensive assessment of contemporary evolution useful for inferring the trajectories of these populations into the future.

Phenotypic Differences Across 36 Generations: Antecedent and modern populations differed phenotypically and patterns of selection tended to favor modern phenotypes. The modern population was taller, had a larger stem, and flowered more

uniformly than the antecedent population. Selection estimates were more often in the

direction of phenotypes of modern plants than those of antecedent plants (Fig. 5, Table

2). However, the direction of selection favored antecedent phenotypes in two situations:

earlier flowering was selected in modern plants in ambient temperatures + well-watered

conditions, and a higher SLA was selected in modern plants in ambient temperature +

low-water conditions (Fig. 5). Ambient temperatures reflect conditions similar to those

found in 1980, which could explain why traits of antecedent individuals were selected for

in those environments. Selection was overall stronger and more frequent in the

antecedent population, and was strongest in antecedent populations in elevated

temperatures + well-watered conditions reflecting modern environments. This difference

in the strength of selection in the antecedent versus modern populations could be the result of either stronger selection on the less-adapted antecedent phenotype or the fact that relatively low variation in the modern population weakened potential signals of selection.

Overall, the antecedent population was more responsive to environmental treatments than the modern population, as environmental treatments affected 55% of traits in antecedent plants and only one trait in modern plants (Table 1). Temperature treatment affected all plants more strongly than watering treatment, while antecedent populations on their own responded more to watering treatments. Plants responded to elevated temperature conditions was generally in an adaptive direction as indicated by increased height and larger stem diameter (Fig. 5). SLA was also decreased in elevated temperatures compared to ambient temperatures to conserve water in high temperatures.

The response of antecedent plants to low-water conditions was in a non-adaptive

direction as indicated by lower branching. Later flowering also occurred in low-water

conditions. Although later flowering times were selected for in our analysis, earlier

flowering is generally adaptive to escape late season drought (Franks et al., 2007).

Modern individuals did not respond to watering treatment, possibly indicating drought

tolerance or a lack of plasticity.

Plasticity and Evolutionary Potential: Family had a strong effect on 88.9% of antecedent traits and 55.6% of modern traits, indicating greater genetic diversity in the antecedent population (Table 1). Additionally, flowering time was highly plastic and variable in the antecedent population, while the modern population displayed no plasticity and little variability in flowering time (Fig. 6). The high degree of plastic

response of the antecedent plants indicates greater genetic variation across family lines,

and, therefore, the potential for continued evolution (Etterson & Shaw, 2001). Flowering time in sunflowers is highly variable among wild populations and is a common selection target for domestication and crop improvement (Blackman, 2013). Both the shift to later flowering time and loss of flowering time plasticity in the modern population could be due to gene introgression from cultivars or genetic drift.

Trait phenotypes of the modern population reflect phenotypes of cultivated sunflower varieties compared to the antecedent population. Wild sunflowers readily hybridize with cultivars, and gene flow from domesticated to wild sunflowers is well- documented (Linder et al., 1998; Burke, Gardner, & Rieseberg, 2002). Introgression could be due to shared pollinators between wild and cultivated seed sunflowers or direct seeding with seeds from cultivated sunflowers and bird seed mixes.

Implications for Adaptation and Persistence: Although the modern sunflower

population displayed phenotypes well-adapted to current conditions, the lack of trait variation casts doubt on the potential for this population to adapt to changing future conditions. Minnesota will experience warming temperatures and drying summers into the future (Galatowitsch et al., 2009). These populations may not be able to shift flowering time as a response to decrease in water availability, and fitness may be detrimentally affected. Additionally, this population is in a fragmented urban landscape.

This will limit the population’s ability to migrate and track climates to which they are adapted. Gene flow from cultivated species will likely continue, and the introduced alleles may have a range of positive, neutral, or negative consequences for adaptation.

The modern population is relatively well adapted to its current environment, but may not be able to adapt to changing environmental pressures.

Table 1: F-statistics and significance levels of Helianthus annuus trait response based on a linear model containing environmental and genetic treatments. A) Trait response of only antecedent plants, and B) trait response of only modern plants. Significant terms are bolded.

P 0.65 0.75 0.89 0.11 0.22 0.50 0.67 0.00 0.00

dfn = 11 3.44 2.77 0.80 0.75 0.52 1.57 1.31 0.95 0.78 F water x family temperature x x temperature

P 0.30 0.53 0.66 0.66 0.37 0.84 0.52 0.82 0.38

family water x x water dfn = 11 1.19 0.91 0.78 0.78 1.09 0.59 0.92 0.61 1.08 F

P 0.92 0.81 0.78 0.58 0.17 0.40 0.89 0.97 0.46

x family dfn = 11 0.48 0.63 0.67 0.86 1.41 1.05 0.51 0.35 0.99 F temperature temperature

P 0.46 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

family dfn = 11 0.99 8.08 3.39 3.00 6.84 20.63 15.83 13.95 10.74 F

0.59 0.65 0.73 0.59 0.98 0.63 1.00 0.00 0.05

water dfn = 1 8.74 3.93 0.28 0.20 0.12 0.29 0.00 0.24 0.00 F temperature x x temperature

P 0.55 0.85 0.40 0.58 0.49 0.32 0.01 0.06 0.05

water water dfn = 1 8.09 3.58 0.37 0.04 3.96 0.72 0.31 0.47 0.99 availability F

P 0.52 0.22 0.20 0.54 0.35 0.46 0.27 0.00 0.00

dfn = 1 0.41 0.22 1.68 0.38 0.87 0.55 1.24 F 12.79 17.34 temperature

183 < dfd < 186

Number

Antecedent

DFF 382 < dfd < 384 Date Bud 384 < dfd < 385 Height 383 < dfd < 384 Stem Diameter 382 < dfd < 383 Number Branch 331 < dfd < 332 Inflorescence 375 < dfd < 376 SLA dfd = 380 Average Seed #/Inflorescence Average Weight/Seed 177 < dfd < 185

P 0.90 0.75 0.37 0.87 0.58 0.22 0.61 0.31 0.64

dfn = 8 0.44 0.63 1.10 0.49 0.83 1.34 0.79 1.19 0.75 F water x family temperature x x temperature

0.23 0.80 0.36 0.73 0.70 0.91 0.37 0.48 0.27

family water x x water dfn = 8 1.33 0.57 1.11 0.66 0.69 0.42 1.10 0.95 1.26 F

P 0.45 0.47 0.92 0.98 0.96 0.99 0.16 0.19 0.61

x family dfn = 8 0.98 0.95 0.40 0.26 0.31 0.99 1.49 1.42 0.79 F temperature temperature

0.59 0.16 0.37 0.19 0.00 0.00 0.00 0.00 0.01

family dfn = 8 0.82 1.49 1.09 1.43 3.51 3.43 5.79 3.17 2.69 F

P 0.38 0.23 0.56 0.85 0.73 0.46 0.14 0.63 0.59

x water x dfn = 1 0.79 1.45 0.34 0.04 0.12 0.54 2.23 0.23 0.29 F temperature temperature

P 0.22 0.51 0.24 0.47 0.89 0.71 0.66 0.91 0.26

water water dfn = 1 1.48 0.43 1.37 0.54 0.02 0.14 0.20 0.01 1.27 availability F

P 0.16 0.85 0.16 0.34 0.51 0.98 0.26 0.29 0.00

dfn = 1 1.96 0.04 2.03 0.90 0.43 0.00 1.29 1.11 F 24.93 temperature

133 < dfd < 136

Modern

B B

dfd < 458

/Inflorescence DFF 456 < Date Bud 465 < dfd < 467 Height dfd = 460 Stem Diameter dfd = 461 Number Branch dfd = 347 Inflorescence Number 445 < dfd < 446 SLA dfd = 466 Average Seed # Average Weight/Seed dfd = 133

Table 2: Linear selection analysis on A) antecedent and B) modern populations of Helianthus annuus in varied environments. Selection gradients (βi) and selection differentials (S) are shown for each population in each environmental treatment. Sample size for each treatment combination is indicated. Significant terms are bolded.

A Antecedent Population trait βi SE P-value S P-value (n=44) height 0.06 0.06 0.27 4.41 0.00 elevated temp stem 0.14 0.04 0.00 0.33 0.00 + high-water DFF -0.057 0.04 0.20 0.88 0.01 SLA 0.08 0.03 0.02 0.00 0.00 (n=45) elevated temp height 0.16 0.06 0.01 4.95 0.00 + low-water stem 0.08 0.04 0.09 0.30 0.00

DFF -0.069 0.04 0.12 0.68 0.03 SLA -0.028 0.03 0.41 0.00 0.64 (n=67) height 0.05 0.05 0.28 3.87 0.00 ambient temp stem 0.12 0.03 0.00 0.36 0.00 + high-water DFF -0.010 0.03 0.71 0.57 0.10 SLA -0.014 0.02 0.56 0.00 0.12 (n=76) ambient temp height 0.01 0.04 0.77 0.09 0.00 + low-water stem 0.15 0.03 0.00 0.12 0.00

DFF 0.00 0.03 0.99 0.05 0.01 SLA -0.041 0.02 0.07 -0.03 0.19

B Modern Population trait βi SE P-value S P-value (n=53) elevated height -0.052 0.05 0.27 2.01 0.02 temp + high- stem 0.15 0.04 0.00 0.33 0.00 water DFF 0.10 0.07 0.16 0.15 0.92 SLA 0.02 0.04 0.67 0.00 0.50 (n=33) height 0.01 0.06 0.86 4.75 0.08 elevated stem 0.17 0.06 0.01 0.37 0.00 temp + low- water DFF -0.186 0.19 0.33 -0.40 0.18 SLA 0.00 0.06 1.00 0.00 0.52 (n=54) height -0.007 0.03 0.85 2.54 0.01 ambient temp stem 0.14 0.03 0.00 0.31 0.00 + high-water DFF -0.113 0.06 0.05 -0.15 0.36 SLA 0.00 0.03 0.96 0.00 0.82 (n=51) height 0.07 0.03 0.03 6.91 0.00 ambient temp stem 0.10 0.04 0.01 0.40 0.00 + low-water DFF -0.045 0.06 0.43 -0.55 0.86 SLA 0.07 0.03 0.03 0.00 0.58

M N

A M C C

Figure 1: Antecedent and modern Helianthus annuus sampling locations in Minneapolis, Minnesota, and common garden experiment location in Duluth, Minnesota. In the inset map of Minnesota, the circle designates Duluth while the square designates Minneapolis. In the inset, antecedent populations are indicated in purple (Antecedent North (AN) & Antecedent Central (AC) and the modern successor populations are indicated in orange (Modern North (MN) and Modern Central (MC).

Figure 2: Common garden block design containing antecedent and modern Helianthus annuus subjected to various environmental conditions. This diagram represents one block containing randomly placed antecedent individuals (purple) and modern individuals (orange) for a total of 160 individuals. The hexagon indicates plants in elevated temperatures induced by open-top fiberglass chambers while the rest are in ambient temperatures. A water drop indicates plants that are well-watered while no symbol indicates those in low-water conditions. There were six blocks for a total n=960.

- A .

B .

Figure 3: PCA biplot based on normalized counts of the top 500 most differentially expressed transcripts between antecedent and modern Helianthus annuus. Plots show relationships A) with all samples and B) with outlier AN-1 removed. Samples within each population group together.

Figure 4: Venn diagram representing transcripts expressed in two pairs of antecedent- modern Helianthus annuus populations. Of 200 genes identified as differentially expressed at a fold change >2 (FDR corrected p-value <0.05), 41.5% were expressed in all groups. 28% of these DE genes expressed specifically in the antecedent population and 7% were expressed specifically in the modern population.

A *** E . .

treatments AW ambient temp + well- watered EW elevated temp + well- watered B F AD ambient *** temp + low- . water ED elevated temp + low- water

C * G . .

D H *** .

Figure 5: Violin plots and least squares means (LSMs) of antecedent and modern Helianthus annuus traits in varied environments. Traits include date of first flower (A, E) stem diameter (B, F) final height (C, G), and SLA (D, H). A-D show the range and distribution pattern, and overall trait differences at the population level. E-H show LSMs of traits in each treatment and arrows indicate the direction of significant total or direct selection on a trait. (p < 0.05* p < 0.001** p < 0.0001***)

A .

B .

treatments AW ambient temp + well-watered EW elevated temp + well-watered AD ambient temp + low-water ED elevated temp + low-water

Figure 6: Date of first flower for different genetic lines of Helianthus annuus derived from antecedent and modern populations. A) Antecedent families displayed variable plastic flowering time response to different environments, while B) modern families in different environmental treatments displayed no plastic response. Lines are colored based on family.

A .

Figure 7: Linear regressions showing the relationship between reproductive traits in antecedent and modern lines of wild Helianthus annuus. A) Julian DFF and average seed number per inflorescence had a positive relationship in antecedent lines (+ 3.599x***), and no significant relationship in the modern population. B) Julian DFF and average weight per seed is negatively correlated in antecedent ( -7.13e-5x***) and modern (- 0.0000853x*) population.

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